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At CES 2026, Siemens and NVIDIA outlined a plan to connect industrial software, factory data, digital twins and AI so manufacturers can test changes virtually before applying them on the shop floor. The ambition is significant, but it is not a finished “Industrial AI Operating System” that companies can install today: the partnership combines existing capabilities with products still being developed and a longer-term vision for adaptive factories.
What Siemens and NVIDIA announced
The companies expanded their strategic partnership around industrial AI and physical AI. Siemens brings industrial software, automation, engineering and operational data, and manufacturing expertise. NVIDIA brings accelerated computing, AI infrastructure, models and frameworks, plus Omniverse simulation libraries. Siemens said it would commit hundreds of industrial AI experts to the work.
The program spans AI-native design and simulation, digital twins, shop-floor copilots, robotics, adaptive manufacturing and supply chains, and AI factories. The headline phrase “Industrial AI Operating System” describes a proposed platform layer connecting these activities; it is not a conventional operating system like Windows or Linux, nor one monolithic product. Siemens’ partnership announcement and NVIDIA’s announcement present it as a strategic direction.
Digital Twin Composer is the central product announcement
Siemens Digital Twin Composer is intended to bring Siemens Xcelerator information, engineering and operational data, and NVIDIA Omniverse libraries together in a photorealistic 3D environment. The goal is to represent products, production processes, factories and supply chains, then simulate proposed changes before committing to physical work.
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That is more ambitious than a 3D visualization dashboard. A useful operational twin would connect a model to live or regularly updated factory information, let engineers test layouts and processes, support virtual commissioning, and help compare alternative production configurations. Siemens describes the product as a bridge between digital-twin information and real-time physical data. Its Digital Twin Composer page also discusses applications such as testing robots in simulation and assessing changes over time.
Availability needs careful qualification. Siemens originally said the product was expected on the Siemens Xcelerator Marketplace around mid-2026, while its product materials also described early access with select customers. That does not establish general availability, final packaging or pricing. Buyers should confirm current status directly with Siemens rather than assume the original target was met. The launch announcement and CES release describe the announcement and target.
How the proposed industrial AI loop would work
The intended shift is from using digital twins mainly for design or occasional analysis to using a continually updated digital representation to inform operations. In principle, the cycle is:
- Design: Engineering systems create product, equipment and process information.
- Simulate: A digital model tests designs, factory layouts, material flows or operating changes.
- Build and commission: Teams use validated models to plan installation and check systems before production.
- Operate: Sensors, controllers and operational software provide information about the physical plant.
- Optimize: AI tools analyze the model and operational data, simulate options and make recommendations.
- Review and update: People validate approved changes, monitor their effects and feed new information back into the model.
In that architecture, Siemens’ industrial systems and data provide the operating context; NVIDIA’s computing and simulation technologies support modeling and AI workloads. The point is not simply adding GPUs to industrial software. It is connecting engineering and shop-floor systems so a simulation can inform decisions about real operations.
What the factory vision means—and what it does not
Siemens and NVIDIA say they aim to develop fully AI-driven, adaptive manufacturing sites, beginning in 2026 with Siemens’ Electronics Factory in Erlangen, Germany, as a blueprint. Their proposed “AI Brain” would combine software-defined automation, operations software, digital-twin and plant data, Omniverse simulation libraries, AI infrastructure and agents that can test or recommend changes.
This is a development program and ambition, not evidence that a fully autonomous factory already exists. In a safe operational model, agents would observe, simulate and recommend; people and established control processes would validate and approve changes before deployment. A system’s ability to suggest a change is not the same as authority to alter production without review.
Siemens also announced nine industrial copilots for activities across the industrial value chain, including help navigating product information and supporting engineering. It highlighted work with Meta on Ray-Ban AI Glasses for real-time audio guidance and safety information for factory workers. These are announced capabilities and development efforts, not proof of broad deployment or measured results at scale. Siemens’ CES announcement provides the company’s description.
PepsiCo offers an early, vendor-reported example
PepsiCo is using Siemens and NVIDIA technology to create high-fidelity digital twins of selected U.S. manufacturing and warehouse facilities. The modeled environment reportedly includes machines, conveyors, pallet routes, operator paths and plant operations, with a view across supply-chain activity. Siemens and NVIDIA say the models let AI agents test and refine proposed changes before physical modifications are made.
The companies report the following results from an initial deployment. These are vendor- and customer-provided claims, not independently audited benchmarks in the cited materials.
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| Reported result | What the claim says | What is not established in the cited material |
|---|---|---|
| Up to 90% of potential issues identified | Issues identified before physical modifications | The facility-specific baseline, definition of “potential issues,” and whether the figure covers issues outside the modeled conditions |
| 20% throughput increase | Increase reported for an initial deployment | The measurement period, baseline, process boundaries, and contribution of the digital twin versus other changes |
| 10%–15% lower capital expenditure | Reduction reported by Siemens and NVIDIA | The calculation basis and whether savings were measured against an approved project or a modeled alternative |
| Nearly 100% design validation | Design validation reported by the companies | The validation method, scope and independent verification |
The claims appear in Siemens’ CES release, its Digital Twin Composer page and an NVIDIA customer story. They are promising signals, not a basis for assuming the same gains at another plant. The published material does not settle how much engineering effort was required, whether the results transfer to other sectors, or how consistently they can be reproduced.
Where digital twins could change factory design and operations
Planning new facilities
For a greenfield factory, teams can build around a planned data architecture and test layouts before construction. Simulations may help compare material flows, operator movement, equipment placement and production configurations, and support virtual commissioning. Siemens says it has used the approach with Foxconn and NVIDIA in planning a robotic facility for manufacturing NVIDIA AI infrastructure systems. That example is described on the Digital Twin Composer page.
Improving existing plants
Brownfield factories face a harder integration problem. Their useful data may be spread across legacy equipment, programmable logic controllers, sensors, manufacturing-execution and enterprise-resource-planning systems, warehouse software, maintenance records, engineering change orders and written or informal procedures. A rendered 3D model is not enough: the model must reflect the actual plant closely enough to support decisions.
Customers named as evaluating capabilities include Foxconn, HD Hyundai, KION Group and PepsiCo. “Evaluating” should not be read as full production deployment. The companies’ partnership announcement names these organizations, but does not establish a common deployment scale or outcome for each.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could prevent adoption
- Poor or incomplete data: Missing, stale or inconsistent equipment information can make an operational twin visually convincing but unreliable.
- Legacy machinery: Older equipment may lack interfaces, sensors or documentation; retrofitting can become a major portion of the project.
- Model drift: Equipment wears, product mixes change, layouts evolve and workers develop workarounds. A model must be recalibrated as the plant changes.
- Simulation limits: A simplified model can miss important interactions; a highly detailed one can be expensive to create and maintain. “Physics-level” modeling is an ambition, not a guarantee of perfect prediction.
- Rare events and human behavior: Normal production models may not predict power failures, supply shortages, quality escapes, cyber incidents or unexpected operator actions well.
- Safety and accountability: Manufacturers still need clear rules for who validates recommendations, authorizes changes, handles exceptions and reverses a deployment if it causes harm.
- Cybersecurity and intellectual property: A connected twin may contain sensitive factory layouts, production recipes, product designs, supply dependencies and maintenance information. Data access, sovereignty and security controls are central design questions.
- Vendor dependence and cost: Integration may deepen reliance on Siemens software and data models, NVIDIA infrastructure and libraries, proprietary connectors, specialist support and ongoing compute and licensing. Public pricing was not established in the cited announcements.
- Workforce effects: Copilots and wearable guidance could support training and reduce information friction, but they also raise questions about worker monitoring, responsibility, skills, and whether operators have authority matching the decisions they are asked to carry out.
Questions manufacturers should ask before committing
- Which CAD, product-lifecycle, manufacturing-execution, enterprise-resource-planning, warehouse and automation systems can connect, and what integration work is required?
- What data must be available, how will asset identities and versions be managed, and who is responsible for correcting inaccurate records?
- How will model accuracy be measured against the physical process, and how often will calibration be repeated?
- Which outputs are simulated, which are measured, and how are uncertainty and out-of-model conditions shown?
- Can the system run in the organization’s required on-premises or private environment, and where will industrial data and models be stored?
- How are recommendations reviewed and approved, and can the system prevent an unvalidated change from reaching production?
- How can models and data be exported if the company changes platforms or suppliers?
- What are the licensing, compute, integration, maintenance and training costs over the expected life of the deployment?
- What happens when a model is wrong, a sensor fails, or actual operating behavior differs from the simulation?
- Which customer outcomes have been independently validated, and what conditions would need to match for those results to be relevant to this facility?
Who is most likely to benefit
The approach is most compelling where production is complex, costly to change, and shaped by many interacting systems; where downtime or commissioning errors carry a high cost; and where a company can instrument operations and reuse a model across design, commissioning and production. Electronics, automotive, food and beverage, logistics, heavy industry, pharmaceuticals, energy infrastructure and data-center hardware manufacturing are plausible fits, but sector membership alone does not establish a business case.
A small or simple facility, a project that only needs basic 3D visualization, or a plant without dependable data may not justify the integration and model-maintenance effort. Greenfield projects can plan their data foundations in advance; brownfield sites may still gain from a focused bottleneck project, but must first address connectivity and data quality.
Why the partnership matters
The announcement reflects a broader convergence: industrial engineering and automation are being connected to GPU-accelerated simulation, AI models, robotics and operational data. If Siemens and NVIDIA can make those layers work together reliably, a digital twin could become a practical decision tool that spans design through operations rather than a model used only at project milestones.
The gap between that promise and a repeatable factory result remains consequential. The CES announcement establishes a product direction, early access and customer activity, alongside a blueprint for future adaptive manufacturing. It does not establish universal economics, independent validation of the reported PepsiCo figures, or an autonomous factory that can safely change itself without human governance.
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